Paul P Eggermont (20 results)

Condition: New. In English.

Condition: New.

Taschenbuch. Condition: Neu. Maximum Penalized Likelihood Estimation | Volume II: Regression | Paul P. Eggermont (u. a.) | Taschenbuch | xx | Englisch | 2011 | Springer | EAN 9781461417125 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

hardcover. Condition: New. In shrink wrap. Looks like an interesting title.

Condition: New. In English.

Hardback. Condition: New. 2009 ed. This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. It covers both theory and practice of nonparametric estimation. The text is novel in its use of maximum penalized likelihood estimation, and the theory of convex minimization problems (fully developed in the text) to obtain convergence rates. A substantial effort has been made to discuss computational details, and to include simulation studies and analyses of some classical data sets using fully automatic (data driven) procedures.…

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: As New. Unread book in perfect condition.

Condition: New. pp. 532.

Hardback. Condition: New. 2009 ed. This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. It covers both theory and practice of nonparametric estimation. The text is novel in its use of maximum penalized likelihood estimation, and the theory of convex minimization problems (fully developed in the text) to obtain convergence rates. A substantial effort has been made to discuss computational details, and to include simulation studies and analyses of some classical data sets using fully automatic (data driven) procedures.…

Condition: new. Questo è un articolo print on demand.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fully develops the theory of convex minimization problems to obtain convergence ratesIncludes simulation studies and analyses of classical data sets using fully automatic (data driven) proceduresMany topics appear for the first time in text. …

Condition: new. Questo è un articolo print on demand.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in s- tistics, operationsresearch, andappliedmathematics, aswellasresearchers and practitioners in the eld. The present volume was supposed to have a short chapter on nonparametric regression but was intended to deal mainly with inverse problems. However, the chapter on nonparametric regression kept growing to the point where it is now the only topic covered. Perhaps there will be a Volume III. It might even deal with inverse problems. But for now we are happy to have nished Volume II. The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. We study smoothing splines and local polynomials in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known. The new twist is thatlettingtheinnerproductdependonthesmoothingparameteropensup new possibilities: It leads to asymptotically equivalent reproducing kernel estimators (without quali cations) and thence, via uniform error bounds for kernel estimators, to uniform error bounds for smoothing splines and, via strong approximations, to con dence bands for the unknown regression function. ItcameassomewhatofasurprisethatreproducingkernelHilbert space ideas also proved useful in the study of local polynomial estimators. …

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in s- tistics, operationsresearch, andappliedmathematics, aswellasresearchers and practitioners in the eld. The present volume was supposed to have a short chapter on nonparametric regression but was intended to deal mainly with inverse problems. However, the chapter on nonparametric regression kept growing to the point where it is now the only topic covered. Perhaps there will be a Volume III. It might even deal with inverse problems. But for now we are happy to have nished Volume II. The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. We study smoothing splines and local polynomials in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known. The new twist is thatlettingtheinnerproductdependonthesmoothingparameteropensup new possibilities: It leads to asymptotically equivalent reproducing kernel estimators (without quali cations) and thence, via uniform error bounds for kernel estimators, to uniform error bounds for smoothing splines and, via strong approximations, to con dence bands for the unknown regression function. ItcameassomewhatofasurprisethatreproducingkernelHilbert space ideas also proved useful in the study of local polynomial estimators.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 592 pp. Englisch. …

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fully develops the theory of convex minimization problems to obtain convergence ratesIncludes simulation studies and analyses of classical data sets using fully automatic (data driven) proceduresMany topics appear for the first time in text. …

Condition: New. pp. 592 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam This item is printed on demand.

Condition: New. Print on Demand pp. 532 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 532.